AMSTERDAM — June 17, 2024 — Monday, at HLTH Europe, the Reliable & Accountable AI Community (TRAIN), a consortium of healthcare leaders, introduced its enlargement to Europe with the target to assist organizations within the area operationalize accountable AI by means of technology-based guardrails. Organizations which have come collectively to type the European TRAIN embrace Erasmus MC (the Netherlands), HUS Helsinki College Hospital (Finland), Sahlgrenska College Hospital (Sweden), Skåne College Hospital (Sweden), Universita Vita-Salute San Raffaele (Italy), and College Medical Heart Utrecht (the Netherlands), with Microsoft because the expertise enabling companion. Basis 29, a nonprofit group that goals to empower sufferers and rework healthcare by means of data-driven initiatives and modern applied sciences, has additionally joined European TRAIN. The community is open to different healthcare organizations in Europe focused on becoming a member of.
Rising AI applied sciences maintain vital promise for revolutionizing the healthcare sector in Europe and throughout the globe. By enhancing affected person care outcomes, streamlining processes and decreasing prices, AI has the potential to rework the trade. Because the expertise continues to evolve, strong growth and analysis requirements are essential to make sure accountable and efficient AI purposes. TRAIN goals to enhance the standard, security and trustworthiness of AI instruments carried out in healthcare to assist guarantee clinicians and sufferers profit from this modern expertise.
TRAIN’s preliminary formation, introduced in March 2024, launched main healthcare organizations within the U.S. as a part of the community. The consortium’s operational aims embrace:
- Offering expertise and instruments that allow reliable and accountable AI rules to be operationalized at scale.
- Working in collaboration with different TRAIN members and key stakeholders to allow all organizations, together with low-resource settings, to profit from technology-based accountable AI guardrails.
- Sharing finest practices associated to using AI in healthcare settings, together with the security, reliability and monitoring of AI algorithms, and the skillsets required to handle AI responsibly. Knowledge and AI algorithms won’t be shared between member organizations or with third events.
- Working towards enabling registration of AI used for scientific care or scientific operations by means of a safe on-line portal.
- Offering instruments to allow measurement of outcomes related to the implementation of AI, together with finest practices for finding out the efficacy and worth of AI strategies in healthcare settings and leveraging of privacy-preserving environments, with concerns in each pre- and post-deployment settings. Instruments that permit analyses to be carried out in subpopulations to evaluate bias may be offered.
- Working towards the event of a federated AI outcomes registry for organizations to share amongst themselves. The registry will seize real-world outcomes associated to efficacy, security and optimization of AI algorithms.
For extra data on European TRAIN, be a part of us at HLTH Europe on Tuesday, June 18, from 12:30 to 12:40 p.m. CEST at The Discussion board stage (H90), the place we’ll share extra particulars concerning the community and its aims.
TRAIN member quotes:
“Remodeling healthcare utilizing AI have to be seen as a worldwide problem that requires worldwide cross-border collaborations. This method is a crucial step that allows us to sort out challenges not solely on the nationwide degree, but additionally throughout the EU and globally. To actually warrant bedside adoption, we should work collectively to make sure AI advantages all. Safeguards constructed into the expertise will enhance such fairness.” — Dr. Michel van Genderen, attending intensivist at Erasmus Medical Heart and co-founder of the Erasmus MC Datahub
“As an NGO devoted to researching using AI in healthcare, Basis 29 is deeply involved with guaranteeing that using AI respects affected person privateness and safety. Whereas high-quality information, usually sourced from sufferers, is important for advancing AI applied sciences, it’s equally essential to ensure accountable use of this information. For us, safeguarding affected person information and fostering a reliable surroundings for AI growth and deployment in healthcare is of paramount significance.” — Sarah Harmon, president, Basis 29
“AI has the potential to rework healthcare, however we should stay vigilant about its moral implications. In TRAIN, we’re becoming a member of forces throughout Europe to share data and instruments for profitable and sustainable implementation of reliable and accountable AI into healthcare practices.” — Magnus Kjellberg, head of AI Competence Heart, Sahlgrenska College Hospital
“TRAIN is a job mannequin for joint efforts between the expertise trade and healthcare. AI represents a method that offers hope for future healthcare effectiveness. The upcoming European TRAIN effort might be a possibility to take a basic step ahead to operationalize accountable AI options by facilitating the era and validation of algorithms with affected person integrity left intact.” — Professor Stefan Jovings, M.D., Ph.D., president of analysis and schooling, Skane College Hospital, Sweden
“TRAIN’s concentrate on accountable AI rules and privacy-preserving collaboration enforces our methods to securely and ethically leverage AI applied sciences. This initiative builds belief, protects affected person information, and aligns the taking part establishments’ practices to the European healthcare requirements.” — Carlo Tacchetti, professor and coordinator, AI strategic program of UniSR and director of Experimental Imaging Heart, San Raffaele Scientific Institute
“AI in healthcare wants collaborations on a nationwide and a global degree to assist us use AI’s energy for bettering affected person care and outcomes. We’re excited to be a part of this collaboration to contribute to the brand new period of drugs that awaits us.” — Ben Collignon, chief data officer, UMC Utrecht
“The first purpose for TRAIN is to allow people and organizations to operationalize accountable AI rules by means of technology-based guardrails. TRAIN can even allow organizations to collaborate by means of federated, privacy-preserving approaches. The formation of TRAIN in Europe will assist foster belief and confidence within the software of AI in well being and make sure that information privateness is maintained.” — David Rhew, M.D., chief medical officer and vp of healthcare, Microsoft
Concerning the Reliable & Accountable AI Community (TRAIN)
The Reliable & Accountable AI Community (TRAIN) is likely one of the first well being AI networks aimed toward operationalizing accountable AI rules. Via collaboration, TRAIN members will assist enhance the standard, security, and trustworthiness of AI in well being by sharing finest practices, enabling registration of AI used for scientific care or scientific operations, offering instruments to allow measurement of outcomes related to the implementation of AI, and facilitating the event of a federated AI outcomes registry for organizations to share amongst themselves.
For extra data, press solely:
Microsoft Media Relations, WE Communications for Microsoft, (425) 638-7777,
rapidresponse@we-worldwide.com
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